Method and system of providing a probability distribution to aid the detection of tumors in mammogram images

ABSTRACT

Methods and systems are disclosed to aid in the detection of cancer or lesion in a mammogram images. Two mammogram images are input into an application that aids in determining the probability of a cancer or lesion being present in one or both of the images. The images are divided into different nodes and labels are applied to the nodes. The first node is compared to different variants of corresponding nodes on the second image as well as neighboring nodes on the first image. Based upon the comparisons, a unary and binary potential is calculated for the label that is applied to the node. The process is repeated for every possible label and for every node. Once the unary and binary potentials have been calculated, the potentials are input into a Conditional Random Field model to determine the probability of cancer for each node of the images.

BACKGROUND

Early detection of cancer is important to providing the best care andbest possible outcome to cancer patients. As such, methods of cancerscreening have been developed and are regularly practiced by physicians.Some methods of cancer screening involve the use of medical imaging todetermine whether a person has or is developing cancer. In the case ofbreast cancer, a mammography machine is used to take images of a woman'sbreasts. Mammogram images are generally analyzed by physicians whodetermine whether or not cancer exists in patients. However, suchanalysis may be incorrect as a result of human error, thus resulting ina false positive (incorrectly identifying cancer when it does not exist)or false negative (failing to identify cancer when it exists) diagnosis.Computer applications may be used to help mitigate the chance of humanerror. It is with respect to this general environment that embodimentsof the present invention have been contemplated.

SUMMARY

Embodiments of the present invention are directed to methods and systemsto detect tumors in mammogram images. In embodiments of the presentdisclosure, tumors or lesions are detected by comparing mammogram imagesof two breasts of the same person or images of the same breast taken attwo different points in time. In embodiments, the image is divided intonodes. The probability of each node depicting an instance of cancer ornormal tissue is determined. That determination is driven by accountingfor multiple variables relating to the node, other nodes on the sameimage, and nodes on the second image. In embodiments, a method isdefined in which a node is simultaneously: (a) labeled as a cancer ornormal, and (b) mapped to a node on the other image. The method iteratesthrough all applicable steps (e.g., switch the label, map to a differentnode on the corresponding image) for each node. In doing so, the methodcompares the local features of each node to typical cancer features aswell as comparing local features of two nodes on different images mappedonto each other. The method also compares the labeling and mapping of anode to that of neighboring nodes on the same image. The results of allof these comparisons are input as unary and binary potentials into aConditional Random Field (“CRF”) model. The method uses a new, efficientalgorithm with the framework of the CRF model to approximate a PosteriorMarginal Probability that a particular node contains a lesion or cancer.The resulting probability takes into account the information of a firstimage as well as information gleaned through different variants ofcomparisons between two images in order to account for all possibledeformations present in each image (e.g., all possible mappings of oneimage into the other).

In embodiments, combined labels are applied to each node of the firstimage. Each combined label is a combination of a mapping label, whichdetermines a corresponding node on the second image, and aclassification label, which “determines or indicates” whether cancerexists in the node of the first image and/or the corresponding node ofthe second image.

In embodiments, a unary potential is calculated for each combined labelapplied to each node of the image. The unary potential describes anapplicable penalty for every value of a classification label and everypossible mapping assigned to each node. In embodiments, the unarypotential of each node is determined using the features of the node onthe first image and the features of the respective (“mapped-to”) node onthe second image.

In embodiments, a binary potential is calculated for each pair ofneighboring nodes in the first image and each pair of combined labelsapplied to these two neighboring nodes. The binary potential is used forsmoothing the classification of a node on the first image and smoothingthe mapping of the node to a node on the second image.

In further embodiments, the unary and binary potentials of each node areused as inputs in a Conditional Random Field discriminative probabilitymodel. In such embodiments, the CRF model computes a probability foreach node of containing a lesion or cancer to aid in determining thebest classification label for each node. Because taking into account alarge number of possible mappings for each node makes known algorithmsof inference using the CRF framework prohibitive due to timerestrictions, in an embodiment, an iterative algorithm is used for afast approximation of pathology probability. This probability can beused in making a final inference (e.g., assigning the bestclassification label to each node).

This summary is provided to introduce a selection of concepts in asimplified form that are further described below in the DetailedDescription. This summary is not intended to identify key features oressential features of the claimed subject matter, nor is it intended tobe used to limit the scope of the claimed subject matter.

BRIEF DESCRIPTION OF THE DRAWINGS

Embodiments of the present invention may be more readily described byreference to the accompanying drawings in which like numbers refer tolike items and in which:

FIG. 1 is an a mammogram image divided into nodes according toembodiments disclosed herein.

FIG. 2 depicts two mammogram images illustrating the mapping of nodesbetween the images, as taught in embodiments of the present disclosure.

FIG. 3 is a flow diagram representing an embodiment of a method 300 forproviding a probability distribution that is used in determining whetheran instance of cancer exists using two mammogram images.

FIG. 4 is a flow diagram representing an embodiment of a method fordefining the unary potential of a node.

FIG. 5 is a flow diagram representing an embodiment of a method fordetermining the binary potential of two selected nodes.

FIG. 6 is a flow diagram representing an embodiment of a method fordetermining the probability of a lesion or cancer using CRF.

FIG. 7 is a functional diagram illustrating a computer environment andcomputer system operable to execute embodiments.

DETAILED DESCRIPTION

This disclosure will now more fully describe exemplary embodiments withreference to the accompanying drawings, in which some of the possibleembodiments are shown. Other aspects, however, may be embodied in manydifferent forms and the inclusion of specific embodiments in thedisclosure should not be construed as limiting such aspects to theembodiments set forth herein. Rather, the embodiments depicted in thedrawings are included to provide a disclosure that is thorough andcomplete and which fully conveys the intended scope to those skilled inthe art. When referring to the figures, like structures and elementsshown throughout are indicated with like reference numerals.

Embodiments of the present disclosure relate to detecting tumors inmammogram images. The methods and systems disclosed compare the featuresof two mammogram images to produce a probability distribution related tothe likelihood that a portion of the image displays a mass of cancercells. In embodiments, a method is defined in which a node issimultaneously: (a) labeled as a cancer or normal, and (b) mapped to anode on the other image. The method iterates through all applicablesteps (e.g., switch the label, map to a different node on thecorresponding image) for each node. The method compares the similarityof the local features of each node on the image to typical or knowncancer features, as well as comparing the similarity between the localfeatures of a node on a first image to a mapped node on a second image.The method also compares the labeling and the mapping of a node toneighboring nodes on the same image. The results of all of thesecomparisons are input as unary and binary potentials into a CRF model.In embodiments, the method uses an efficient algorithm within theframework of CRF model to approximate a Posterior Marginal Probabilitythat a particular node contains a lesion or cancer. The resultingprobability takes into account the information of a first image as wellas information gleaned through different variants of comparisons betweentwo images in order to account for all possible deformations present ineach image (e.g., all possible mappings of one image into the other).

In embodiments, the resulting probability distribution is used indetermining whether or not cancer is present in one or both of themammogram images. In other embodiments, the methods and systemsdisclosed are used to detect lesions, calcifications, tumors, cysts, orother ailments, each of which terms are used interchangeably herein.While certain methods and systems disclosed herein may be directedtowards detecting cancer in mammogram images, one skilled in the artwill recognize that the methods and systems may also be practiced onX-ray images, computer axial tomography (“CAT”) scans, magneticresonance imaging (“MRI's”), or any other type of medical imaging. Inembodiments, the methods and systems may also be practiced when twoinput images of the system are not two breasts of the same person, buttwo images one breast taken at different moments in time. In furtherembodiments, the methods and systems disclosed herein may be applied toimages of any organ or tissue to aid in pathology.

Referring now to FIG. 1, a mammogram image 100 is illustrated.Generally, mammogram images are X-ray images of a breast produced by amammography machine. Mammograms may be performed using screen-filmcassettes or digitally in a process known as Full Field DigitalMammography. One skilled in the art will readily appreciate that themethods and systems disclosed operate regardless of the method used inproducing the mammogram image 100. In embodiments, mammogram images,such as mammogram image 100, are used as inputs to the disclosed methodsand systems. Upon receiving mammogram image 100, the image is dividedinto nodes, such as node 106. In one embodiment, the image may bedivided into a grid by vertical lines, such as vertical line 102, andhorizontal lines, such as horizontal line 104. Each node may bedesignated by a cell in the grid. In embodiments, the image is dividedinto nodes such that each pixel representing the image corresponds to anode. In embodiments, the disclosed methods and systems perform variousoperations and calculations upon the nodes, discussed below in relationto FIGS. 3-6, to determine whether the node depicts a tumor.

In embodiments, nodes, such as node 106 and node 108, are assigned alabel. In embodiments, labels consist of two parts: a classificationlabel and a mapping label. In embodiments, a classification labelclassifies a node as embodying a certain classification, e.g. cancer ornormal. In embodiments, a mapping label assigned to a node is a vectorthat determines a mapping to a node on the second image. In embodiments,classification labels are used to characterize nodes. For example, eachclassification label may have two values, e.g., cancer\non-cancer ortumor\normal. In other embodiments, multiple classification labels canbe used to simultaneously characterize an individual node of a firstmammography image and a respective node on a second mammography image.Ultimately, the number and types of classification labels used aredetermined based upon the underlying algorithm employed to detect imageanomalies. For example, different classification labels may be appliedfor detecting cancer, lesions, calcifications, or other ailments. Inembodiments, mapping labels describe a translation applied to a node inorder to map the node to a node on another image. Translations and theprocess of mapping are further described with reference to FIGS. 2 and5.

In embodiments, the methods and systems disclosed herein calculatepotentials for each node, such as node 106 and node 108. In embodiment,potentials are calculated and assigned to nodes as penalties used indetermining a proper classification label for the nodes. For example, aunary potential of a particular classification label value (e.g., canceror normal) assigned to nodes is affected by the characteristics of thetwo mammography images. In embodiments, characteristics such asintensity, gradient of intensity, or contrast may be used to determinethe likelihood of a node being correctly classified as cancer or normal.In other embodiments, characteristics may also include general featuresof the image such as average intensity and average contrast of theimage. In further embodiments, pattern recognition, digital geometry,signal processing, or any other process of image detection or analysisknown to the art may be used to assign penalties to differentclassification labels of a node. In embodiments, every applicable labelis applied to a node, such as node 106, and the potential or penaltyapplicable to each label is calculated for each node. Calculating theunary potential is described with reference to FIGS. 3-4.

In embodiments, binary potentials may be calculated based upon therelationship between nodes. For instance, while unary potentials may becalculated for each node 106 and 108, a binary potential is calculatedfor the relationship between nodes 106 and 108. The relationship betweennodes 106 and 108 is illustrated by edge 112. Calculating binarypotential is further described with reference to FIGS. 3 and 5.

FIG. 2 illustrates an embodiment of two mammogram images that may beused in embodiments of the present disclosure. In embodiments, a firstmammogram image 202 and a second mammogram image 208 are compared todetermine a probability distribution related to whether a tumor existsin the first image. Each mammogram image 202 and 208 may be divided intoseparate nodes, such as nodes 106 and 108 (FIG. 1). The nodes may bedetermined by a grid, as demonstrated by grid lines 204, 206, 210, and212. In other embodiments, each node may correspond to the individualpixels that make up mammogram images 202 and 208. In one embodiment, thefirst mammogram image 202 is a mammogram of a woman's left breast whilethe second mammogram image 208 is an mammogram of her right breast, orvice versa. In this embodiment, characteristics of one breast arecompared to the characteristics of the other breast. For example, if anode in the first mammogram image 202 is labeled as non-cancer itscharacteristics are compared to characteristics of a corresponding nodein the second mammogram image 208. In embodiments where the node ismapped to a node in the second image, the mapping label representing thenode is a vector, such as vector 214. In embodiments, a labelrepresented as a vector comprises four values, a pair of valuesrepresenting classification labels (e.g., classifying a node in thefirst image as cancer or normal, e.g., c₁, and classifying acorresponding node in the second image as cancer or normal, e.g., c₂), ax-axis translation describing a change in position along the x-axis ofthe node from the first image 202 to a corresponding node in the secondimage 208 (e.g., dx), and a y-axis translation describing a change inposition along the y-axis of the node from the first image 202 to acorresponding node in the second image 208 (e.g., dy). Thus, a label maybe defined by the formula l={c₁, c₂, dx, dy}, where the variable lstands for a mapping label.

In embodiments, a comparison is accomplished by mapping the node in thefirst mammogram image 202 to corresponding node(s) in the secondmammogram image 208, as illustrated by vector 214. It is highly unlikelythat cancer would be present in the same location in both the left andright breast. Thus, if a node in the first mammogram image 202 hassimilar characteristics to a corresponding node on the second mammogramimage 208, there is a higher probability that both nodes are non-cancer.

In embodiments, a node in the first mammogram image 202 is compared todifferent nodes within a predetermined region of the second mammogramimage 208. In embodiments, the predetermined region is defined as aregion surrounding the node on the second image which has the samecoordinates as the first node on the first image. Different variants ofcorrespondence between the nodes account for different possibilities ofsmall deformations present in each breast as a result of the mammographyprocess. Vector 216 illustrates a mapping of a node in the firstmammogram image 202 to a node within a predetermined region in thesecond mammogram image 208. In embodiments, the predetermined region issmall, as discussed further with reference to FIG. 5. Largerpredetermined regions lead to less accurate results as well as a greaterburden on processing resources.

In other embodiments, the second mammogram image 208 is a mammogramimage of the same breast depicted in mammogram image 202 taken at alater point in time. Comparison of the same breast at different timesallows for a more accurate determination of the existence of a tumorbecause natural differences between a left and right breast do not needto be accounted for.

Referring now to FIG. 3, a flow diagram representing an embodiment of amethod 300 for providing a probability distribution that is used indetermining whether an instance of cancer exists in a mammogram imageusing two mammogram images is illustrated. At receive images operation302, the method receives mammogram images for analysis. In oneembodiment, the method receives two mammogram images, such as the firstmammogram image 202 (FIG. 2) and the second mammogram image 208 (FIG.2). At determine nodes operation 304, each mammogram image is dividedinto a set of nodes. In one embodiment, nodes may be arbitrarilycreated, for example, by superimposing a rectangular grid over eachimage. In other embodiments, each node corresponds to a pixelrepresenting the image. In one embodiment, determine nodes and neighborsoperation also determines a set of neighbors for each node. A set ofneighbors for each node may consist of its up-down and left-rightneighbors in the grid. In other embodiment, a set of neighbors mayinclude diagonal neighbors in the grid. In embodiments, an arbitrarygraph may be superimposed over an image, edges of this graph indicatingpair of nodes which are considered to be neighbors. FIGS. 1 and 2 arenot drawn to scale and considerably more nodes per image may be employedin embodiments.

After dividing the mammogram images into nodes and determining apredefined region of neighbors, flow proceeds to calculate localfeatures operation 306. At this point, each node is analyzed todetermine its features. In embodiments, the features of the node may bebased upon intensity, the gradient of intensity, contrast, etc. In otherembodiments, the features may be calculated for a neighborhood of nodessurrounding the node on the image. While the embodiment has beendescribed using these features, one skilled in the art will recognizethat other features of the image may be analyzed. In some embodiments,the image is analyzed resulting in a feature list for each node. Inembodiments, these features may be used to compare a node on a firstimage to a node on a second image or to compare the node on a firstimage or a corresponding node on a second image to a typical cancernode.

After calculating the local feature, flow proceeds to operation 308. Inthe embodiment shown in FIG. 3, calculate descriptive informationoperation 308 comprises two operations, calculate unary potentialoperation 310 and calculate binary potential operation 312. Inembodiments, calculate descriptive information operation may comprisecalculating other type of information, such as intensity, gradient ofintensity, contrast, etc. Calculate unary potential operation 310 isfurther described in FIG. 4. Calculate binary potential operation 312 isfurther described with reference to FIG. 5. While the embodimentdescribed with regard to FIG. 3 illustrates two operations performed inoperation 308, one skilled in the art will appreciate that any number ofcalculations may be performed in operation 308. (e.g., othercalculations described herein with respect to the disclosed systems andmethods may be simultaneously calculated in step 308). In furtherembodiments, the unary potential operation 310 is performed for everynode of the first image and binary potential operation 312 is performedfor every pair of neighboring nodes of the first image receivedoperation 302.

Flow proceeds to determine probability distribution operation 314.Determine probability distribution operation 314 receives the resultsand/or functions that are generated in operation 308 as inputs andderives a probability distribution. For example, in embodiments theunary and binary potentials, determined in steps 310 and 312respectively, may be used as inputs to determine probability operation312. In other embodiments, determine probability distribution operation312 may receive inputs from any other calculation taking place inoperations 306 or 308.

In embodiments, determine probability operation 314 uses a probabilisticmodel to produce a Posterior Marginal Probability function, for examplea Markov Random Field (“MRF”) or any other probabilistic model known inthe art. In other embodiments, a Conditional Random Field is used toproduce the Posterior Marginal Probability distribution, as explainedfurther with regards to FIG. 6. Because a mapping is included in alabel, the number of possible labels is fairly large, which, in turn,makes known algorithms for the calculation of a Posterior MarginalProbability function and/or the use of an inference in the CRF framework(such as Loopy Belief Propagation and Iterative Conditional Mode)prohibitive due to time constraints.

In an embodiment, an original iterative algorithm for fast approximatecalculation of Posterior Marginal Probability of labels for each node isused. In embodiments, the iterative algorithm makes use of differentdivisions (or “cuttings”) of a CRF graph (which consists of nodescorresponding to the first image and edges between neighboring nodes,such as nodes 106 and 108 and edge 110) into a sequence of acyclicsubgraphs. In one embodiment, the subgraphs may comprise divisions alongvertical or horizontal lines. In embodiments, an iteration of thealgorithm consists of a sequence of calculations upon each subgraph inturn. The calculation proceeds under the assumption that anapproximation of the Posterior Marginal Probabilities of nodes in theremaining part of the graph has already been calculated. In otherembodiments, versions of the algorithms that calculate PosteriorMax-Marginal Probabilities and/or the most probable label assignmentsassume that the most probable labels have already been assigned andfixed to the remaining part of the graph. Under these assumptions, thealgorithm makes use of the acyclicity of chosen subgraph and of the factthat a Dynamic Programming method can efficiently and exactly calculatethe Posterior Marginal Probability (or the most probable labelassignment) for any acyclic subgraph. After applying a DynamicProgramming method to the subgraph (e.g., a horizontal line across theimage), the calculated values are stored and used in calculations uponthe next subgraph (e.g., the next horizontal line). In embodiments, theiterations are repeated for all subgraphs in a division (e.g., allhorizontal lines). In other embodiments, iterations of the algorithm mayalternate between different divisions, for instance, after completing aniteration upon a division comprising horizontal lines, the nextiteration is performed upon a division made up of vertical lines. Theuse of Dynamic Programming for large acyclic (e.g., linear) subgraphsallows for a substantial reduction in the number of iterations necessaryto perform convergence comparisons using other algorithms known in theart (e.g., Loopy Belief Propagation algorithm and Iterative ConditionalMode algorithm).

In embodiments, after calculating the Posterior Marginal Probability oflabels for each node of the first image, a marginalization is made overall of the mappings of each node in the first image and over allpossible label assignments in corresponding nodes on the second imageresulting in a Posterior Marginal Probability of a classification foreach node.

Flow then proceeds to provide cancer nodes function 316 where, inembodiments, an inference is made about existence of cancer and, if itdoes exist, the location of the cancer on the mammography image. Inembodiments, the inference can be made by defining a threshold andlabeling as cancer all nodes with Posterior Marginal Probability ofcancer greater than the defined threshold. In other embodiments, themost probable label is chosen for each node and only nodes whereclassification part of the most probable label is “cancer” are marked ascancer. These results can be provided or displayed to a user or anotherapplication.

FIG. 4 is a flow diagram illustrating an embodiment of a method 400 fordefining the unary potential of a node. In an embodiment, the unarypotential for each node of the first image is determined in calculationoperation 406 (FIG. 4). In embodiments the value of the unary potentialof the node will be calculated for every possible label applicable tothe node. While an unlimited amount of labels can be applied to eachnode, in an embodiment, a single ternary classification label is appliedto a node, for example, cancer in a first node on the first image and nocancer in the corresponding node of the second image, no cancer in thefirst node and no cancer in the corresponding node of the second image,cancer in the first node and cancer in the corresponding node of thesecond image, and no cancer in the first node and cancer in thecorresponding node of the second image. In embodiments, thecorresponding node on the second image is determined by a mapping partof the label, which may be different for different mappings. Forexample, for a given first node on the first image, a predeterminedregion of nodes in the second mammogram image where the first node canbe mapped may consists of 49 nodes (7 possible shifts along x-axis and 7possible shifts along y-axis). In this instance, the unary potentialmust be computed for 4 classification labels multiplied by 49 variantsof mapping labels, thus resulting in 196 different possible labelcalculations for the first node in the first image. In embodiments, thisnumber also affects the number of calculations needed to calculatebinary potential. In embodiments, binary potential is determined bylabels of two neighboring nodes of the first image. If each node has oneof 196 different labels then the number of different label assignmentsfor two neighbor nodes is equal to 196 multiplied by 196, resulting in alarge number of binary potential calculations. Calculating binarypotentials is described further with regards to FIG. 5. In embodiments,the iterative algorithm is utilized to reduce the number ofcalculations.

Flow begins at select node in image operation 402, where a node isselected from the first mammogram image. Once a node is selected, flowproceeds to assign labels operation 404. Because the unary potential isdefined for every label, every possible label is assigned to theselected node in operation 404. In one embodiment, labels areindividually assigned and the unary potential is calculated. Aftercalculating the unary potential for the node with the label (e.g.,cancer), a new label (e.g., normal) is assigned to the node and theunary potential is again calculated for the new label This process isrepeated until all labels for all nodes have been exhausted for eachimage.

Flow proceeds to operation 406, where unary potential for each label isdetermined. In embodiments, unary potential is calculated by differentformulas depending on classification part of a label, as described inoperations 408 and 410.

Operation 408 calculates the unary potential for a label applied to theselected node on the first image if the selected node on the first imageor a corresponding node on the second image or both nodes are labeled ascancer. In one embodiment, the unary potential U of a label lclassifying the selected node in the first image as cancer is calculatedwith the following formula:

If I _(x,y) <I _(canc) then U=(I _(x,y) −I _(canc))²/(σ_(canc))²

If I _(x,y) >I _(canc) then U=0

where I_(x,y) is the intensity of a node identified by its x,ycoordinate position on the first image, I_(canc) is the lower limit oftypical cancer intensities range where typical cancer intensities rangeis known, and σ_(canc) is a known constant. The unary potential U of alabel l in a node where classification part of label l corresponds tocancer in the corresponding node on the second image is calculated withthe following formula:

If I _(x+dx,y+dy) <I _(canc) then U=(I _(x+dx,y+dy) −I_(canc))²/(σ_(canc))²

If I _(x+dx,y+dy) >I _(canc) then U=0

where I_(x+dx,y+dy) is the intensity of the corresponding nodeidentified by its x+dx, y+dy coordinate position on the second image anddx and dy are x-axis translation and y-axis translation portions of themapping part of the label l. The unary potential U of a label l in anode where the classification part of label l corresponds to cancer inthe node on the first image as well as in the corresponding node on thesecond image is equal to the sum of the two formulas.

Although one embodiment is described using intensity as the node'sfeature characteristic, one skilled in the art will recognize that otherfeatures of the node may be used to calculate the unary potential. Inembodiments where other features are used, different formulas known inthe art may be employed to calculate the unary potential. Inembodiments, a local recognizer configured to recognize cancer based onthe features of a node can be used to calculate unary potential. Typesof local recognizers are known in the art.

The previously described formulas provide a penalty related to thelikelihood that the features of the node are similar to features ofcancer. For example, when intensity is the feature measured, if typicalintensities of cancer are known, I_(canc) may be a constant thatcorresponds to the lower limit of a typical cancer intensity range, inwhich case the formula results in a comparison of how close the node'sfeature resembles that of cancer. In other embodiments, I_(canc) maydepend on the features of the image or images. For instance, theintensity of the node may be compared to an average intensity of theimage or images as a whole. In such an embodiment, because the greaterportion of the image is most likely not cancer, if the intensity of thenode is not significantly greater than the average intensity of theentire image, it is unlikely that the node depicts an instance ofcancer.

Operation 410 calculates the unary potential for a label applied toselected node assuming it classifies both the selected node and acorresponding node on the second image as normal. In this case, theunary potential is computed by the following formula:

(I _(x,y) −I _(x+dx,y+dy))²/(σ_(sim))²

where I_(x,y) is the intensity of the node on the first image identifiedby its x,y coordinate position and I_(x+dx,y+dy) is the intensity of thecorresponding node on the second image identified by the translations dxand dy applied to the x,y coordinate of the selected node, and σ_(sim)is an experimentally determined constant. An experimentally determinedconstant, in embodiments, is a constant that may be determined through aprocess of trial and error, training (e.g., a training process used withvarious images), or any other type of learning process known in the art.The formula compares the node on the first image with a correspondingnode on the second image. If both nodes are not cancer, their relativeintensities should be similar, thus resulting in a small number. Inembodiments, the comparison is used to judge the probability that eachnode is correctly labeled. The result of the comparison may depend onthe nature of the two images that are compared. In one embodiment, thetwo images may be a first image corresponding to a left breast and asecond image corresponding to a right breast. In this embodiment, if thefirst and second images are locally similar in the neighborhoods ofcorresponding nodes, it is less probable that the image depicted ineither of these two nodes is in fact cancer. This is because it ishighly unlikely that cancer would develop in the same place in eachbreast.

While the unary potential has been defined in relation to a preferredembodiment of using a single ternary classification label (e.g.,classification labels with two values for a node on the first image andtwo values for the corresponding node on the second image), one skilledin the art will appreciate that in other embodiments a unary potentialcan be calculated for any classification label applied to a node,including labels with more than two values for a node on the first imageand for the corresponding node on the second image. Even if the numberof labels changes, the unary potential may be calculated for a nodeuntil a unary potential has been defined for every possible combinationof labels.

After calculating the unary potential for every label applicable to anode, flow proceeds to decision step 412 where a determination is madewhether more nodes exist in the image. If a node exists for which theunary potential has not been determined, flow branches YES to step 402,the next node in the first image is selected, and the process ofcalculating unary potentials is repeated for the next node on the image.If unary potentials have been calculated for all nodes in the firstimage, flow branches NO and flow terminates.

Referring now to FIG. 5, a flow diagram representing an embodiment of amethod 500 for determining the binary potential of two selected nodes isillustrated. While the unary potential is calculated relative to asingle node, binary potential is calculated in regards to two nodes. Inembodiments, the binary potential serves two functions. First, thebinary potential is used to provide spatial smoothness of classificationlabels. The binary potential provides label smoothing by applying apenalty to a pair of non-coinciding classification labels in twoneighboring nodes to decrease the number of cancer/non-cancertransitions between neighboring nodes. Second, the binary potentialprovides for smooth mapping. The binary potential provides for smoothmapping by ensuring that the translational shift from a node on thefirst image to a node on the second image is not too different from thetranslational shift applied to a neighboring node on the first image.For example, the binary potential will result in a large penalty if theshifts in two neighboring nodes differ greatly, thus resulting in alower probability value.

Flow begins at select pair of neighboring nodes in image operation 502.In select two nodes in image operation 502, two nodes are selected fromthe same image, such as the first mammogram image 202 (FIG. 2). Inembodiments, a first node is selected and then a second node is selectedfrom within a set of neighbors of the first node. In embodiments, theneighborhood may be determined by a predefined tolerance of locality. Inan embodiment, the image is previously divided into nodes and set ofneighbors is determined for each node by determine node and neighborsoperation 304 (FIG. 3).

Flow then proceeds to select pair of labels operation 504. Because thebinary potential is calculated for every pair of labels, every pair oflabels assignable to the selected nodes is assigned in operation 504. Inone embodiment, labels are individually assigned and the binarypotential is calculated. After calculating the binary potential relatedto the neighboring nodes with respective labels, a new pair of labels isassigned to the nodes and the binary potential is again calculated forthe new pair. This process is repeated until all possible pairs oflabels have been exhausted.

In compare label to neighbor's label operation 506, the labels of theselected nodes are compared to calculate a binary potential. Inembodiments, the labels are represented in vector form. The first labell₁, is further defined as:

l₁={c₁₁,c₂₁,dx₁,dy₁}

where c₁₁ is a binary label for the node on the first image (e.g.,cancer/normal), c₂₁ is a binary label (e.g., cancer/normal) for thecorresponding node on the second image, dx₁ is a shift from a first nodeto a second node along the x-axis, and dy₁ is a shift from the firstnode to a second node along the y-axis. The second label l₂ (for aneighboring node) is defined as:

l₂={c₁₂,c₂₂,dx₂,dy₂}

where c₁₂ is a binary label (e.g., cancer/normal) for the neighboringnode on the first image, c₂₂ is a binary label (e.g., cancer/normal) forthe corresponding node on the second image, dx₂ is a shift from a firstnode to a second node along the x-axis. In embodiments, the binarypotential for the pair of labels (l₁,l₂) is calculated using thefollowing formula:

B(l ₁ ,l ₂)=Bc(c ₁₁ ,c ₁₂ ,c ₂₁ ,c ₂₂)+Bm(dx ₁ ,dy ₁ ,dx ₂ ,dy ₂

where l₁ and l₂ are labels that correspond to a selected first node anda selected second node, Bc is a classification part of binary potentialwhich depends only on classification parts of corresponding labels(further described in operation 508) and Bm is a mapping part of binarypotential which depends only on mapping (translational) parts ofcorresponding labels (further described in operation 510).

In Bc operation 508 the classification part of binary potential iscalculated. The classification part of a label of the selected node iscompared to classification parts of labels of neighboring nodes toensure a smoothness of the resulting classification. For example, a nodelabeled as normal that is surrounded by nodes labeled as cancer is notlikely to actually be normal tissue, the reason being that mass tumorsusually form compact regions. In this case, a larger binary potential isdetermined, thus decreasing the probability that each of the neighboringnodes are properly labeled. Conversely, if the node's label matches thatof the labels surrounding it, then it is more likely that the node iscorrectly labeled, a lower binary potential is determined, thusincreasing the probability that each of the neighboring nodes areproperly labeled.

In embodiments, the spatial smoothing of classification function forlabel l₁, of the first node and label l₂ of the second node isrepresented in vector form:

l₁={c₁₁,c₂₁,dx₁,dy₁} and l₂={c₁₂,c₂₂,dx₂,dy₂}

is defined in the following way:

if c ₁₁ =c ₁₂ and c ₂₁ =c ₂₂ Bc(c ₁₁ ,c ₁₂ ,c ₂₁ ,c ₂₂)=0

if c ₁₁ ≠c ₁₂ and c ₁₁ ≠c ₁₂ Bc(c ₁₁ ,c ₁₂ ,c ₂₁ ,c ₂₂)=2*P

otherwise Bc(c ₁₁ ,c ₁₂ ,c ₂₁ ,c ₂₂)=P

where P is some experimentally chosen positive constant. Anexperimentally determined positive constant, in embodiments, is apositive constant that may be determined through a process of trial anderror, training (e.g., a training process used with various images), orany other type of learning process known in the art.

In embodiments, Bm operation 510 calculates the mapping part of thebinary potential to smooth the mapping of nodes from a first image to asecond image. Smoothing the mapping of nodes ensures that nodes locatedclose together in the first image are mapped such that theircorresponding mappings to nodes on the second image are similarlylocated close together.

In embodiments, the map smoothing function for label l of the first nodeand label l₂ of the second node represented in vector form:

l₁={c₁₁,c₂₁,dx₁,dy₁} and l₂={c₁₂,c₂₂,dx₂,dy₂} is defined as:

Bm(dx ₁ ,dy ₁ ,dx ₂ ,dy ₂)=(dx ₁ −dx ₂)²+(dy ₁ −dy ₂)²

If the function results in a small value, the selected neighboring nodesin the first image were mapped closely together in the second image,thus resulting in a lower binary potential and a higher confidence inaccurate comparisons. Conversely, if the function results in a largevalue, the selected nodes in the first image were not mapped closelytogether in the second image, thus resulting in a higher binarypotential and a lower confidence in accurate comparisons.

Flow then proceeds to decision step 512. In embodiments, the binarypotential is calculated for every label pair applicable to the selectednodes. Thus, decision step 512 determines whether there are more pairsof labels applicable to the selected nodes. If there are more pairs oflabels, flow branches YES to operation 504. New labels are assigned tothe selected nodes and the process of calculating a binary potential isrepeated. If the binary potential has been calculated for allcombinations of labels for each node, then flow branches NO to decisionstep 514.

After calculating the binary potential for every possible combination oflabels for the selected pair of nodes, flow proceeds in this embodimentto decision step 514 where a determination is made whether more pairs ofneighboring nodes exist in the image. If pair of neighboring nodesexists for which the binary potential has not been determined, flowbranches YES to step 502, the next pair of neighboring nodes in thefirst image is selected, and the process of calculating binarypotentials is repeated for the next selected pair of nodes on the image.If binary potentials have been calculated for all pairs of neighboringnodes in the first image, flow branches NO and flow terminates.

FIG. 6 is an illustration of a flow diagram representing an embodimentof a method 600 for determining the probability of a lesion or cancerusing CRF as a probability model. Flow begins at operation 602 where theunary potential is calculated for each node. The calculation of theunary potential was previously explained with reference to FIG. 4. Flowthen proceeds to operation 604 where the method calculates the binarypotential of each pair of neighboring nodes. Calculation of the binarypotential was previously explained with reference to FIG. 5. Aftercalculating the unary and binary potential, flow proceeds to calculatePosterior Marginal Probability operation 606 where the calculatedpotentials are used as inputs into a CRF model in order to calculate aPosterior Marginal Probability of cancer in each node. In embodiments,the iterative algorithm makes use of different divisions (or “cuttings”)of a CRF graph (which consists of nodes corresponding to the first imageand edges between neighboring nodes, such as nodes 106 and 108 and edge110) into a sequence of acyclic subgraphs. In one embodiment, thesubgraphs may comprise divisions along vertical or horizontal lines. Inembodiments, one iteration of the algorithm consists of a sequence ofcalculations upon each subgraph in turn. The calculation proceeds underthe assumption that an approximation of the Posterior MarginalProbabilities of nodes in the remaining part of the graph has alreadybeen calculated. In other embodiments, versions of the algorithms thatcalculate Posterior Max-Marginal Probabilities and/or the most probablelabel assignments assume that the most probable labels have already beenassigned and fixed to the remaining part of the graph. Under theseassumptions, the algorithm makes use of the acyclicity of chosensubgraph and of the fact that a Dynamic Programming method canefficiently and exactly calculate the Posterior Marginal Probability (orthe most probable label assignment) for any acyclic subgraph. Afterapplying a Dynamic Programming method to the subgraph (e.g., ahorizontal line across the image), the calculated values are stored andused in calculations upon the next subgraph (e.g., the next horizontalline). In embodiments, the iterations are repeated for all subgraphs ina division (e.g., all horizontal lines). In other embodiments,iterations of the algorithm may alternate between different divisions,for instance, after completing an iteration upon a division comprisinghorizontal lines, the next iteration is performed upon a division madeup of vertical lines. The use of Dynamic Programming for large acyclic(e.g., linear) subgraphs allows for a substantial reduction in thenumber of iterations necessary to perform convergence comparisons usingother algorithms known in the art (e.g., Loopy Belief Propagationalgorithm and Iterative Conditional Mode algorithm).

Flow then proceeds to provide cancer nodes operation 608. The PosteriorMarginal Probability distribution of labels in a node calculated inoperation 606 is used to determine a best classification label for eachnode. In one embodiment the best classification label can be defined asclassification part of the label that maximizes Posterior MarginalProbability function in a given node. In other embodiments, PosteriorMarginal Probability distribution of labels for each node can be used toobtain a Posterior Marginal Probability of cancer for each node bymarginalizing over all possible mappings and over all possible cancerassignments in corresponding nodes on the other image. The PosteriorMarginal Probability of cancer may be used to classify a node as cancerif the latter probability is greater than a predetermined threshold. Inan embodiment, the predetermined threshold may be determined by anapplication, by a user, or through the use of experimental data. Thedetermination of best classification ensures that factors such as thecomparison of a node to all possible corresponding nodes on a secondimage, the comparison of a node to features of nearby nodes on the sameimage (e.g., the local cancer recognizer), and the comparison of nearbylabels are accounted for when assigning a best classification to eachnode.

With reference to FIG. 7, an embodiment of a computing environment forimplementing the various embodiments described herein includes acomputer system, such as computer system 700. Any and all components ofthe described embodiments may execute as or on a client computer system,a server computer system, a combination of client and server computersystems, a handheld device, and other possible computing environments orsystems described herein. As such, a basic computer system applicable toall these environments is described hereinafter.

In its most basic configuration, computer system 700 comprises at leastone processing unit or processor 704 and system memory 706. The mostbasic configuration of the computer system 700 is illustrated in FIG. 7by dashed line 702. In some embodiments, one or more components of thedescribed system are loaded into system memory 706 and executed by theprocessing unit 704 from system memory 706. Depending on the exactconfiguration and type of computer system 700, system memory 706 may bevolatile (such as RAM), non-volatile (such as ROM, flash memory, etc.),or some combination of the two.

Additionally, computer system 700 may also have additionalfeatures/functionality. For example, computer system 700 includesadditional storage media 708, such as removable and/or non-removablestorage, including, but not limited to, magnetic or optical disks ortape. In some embodiments, software or executable code and any data usedfor the described system is permanently stored in storage media 708.Storage media 708 includes volatile and non-volatile, removable andnon-removable media implemented in any method or technology for storageof information such as computer readable instructions, data structures,program modules, or other data. In embodiments, mammogram images and/orresults of probability determination are stored in storage media 708.

System memory 706 and storage media 708 are examples of computer storagemedia. Computer storage media includes, but is not limited to, RAM, ROM,EEPROM, flash memory or other memory technology, CD-ROM, digitalversatile disks (DVD) or other optical storage, magnetic cassettes,magnetic tape, magnetic disk storage, other magnetic storage devices, orany other medium which is used to store the desired information andwhich is accessed by computer system 700 and processor 704. Any suchcomputer storage media may be part of computer system 700. In someembodiments, mammogram images and/or results of probabilitydetermination are stored in system memory 706. In embodiments, systemmemory 706 and/or storage media 708 stores data used to perform themethods or form the system(s) disclosed herein, such as unary potentialdata, binary potential data, probability distributions, etc. Inembodiments, system memory 706 would store information such as imagedata 714 and application data 716. In embodiments, image data 714 maycontain actual representations of an image, such as a first mammogramimage 202 or a second mammogram image 208 (FIG. 2). Application data716, in embodiments, stores the procedures necessary to perform thedisclosed methods and systems. For example, application data 716 mayinclude functions or processes for calculating unary and binarypotentials, functions or processes for performing the iterativealgorithm, and/or functions or processes for the CRF model.

Computer system 700 may also contain communications connection(s) 710that allow the device to communicate with other devices. Communicationconnection(s) 710 is an example of communication media. Communicationmedia may embody a modulated data signal, such as a carrier wave orother transport mechanism and includes any information delivery media,which may embody computer readable instructions, data structures,program modules, or other data in a modulated data signal. The term“modulated data signal” means a signal that has one or more of itscharacteristics set or changed in such a manner as to encode informationor a message in the data signal. By way of example, and not limitation,communication media includes wired media such as a wired network ordirect-wired connection, and wireless media such as an acoustic, RF,infrared, and other wireless media. In an embodiment, mammogram imagesand or determinations of probability results may be transmitted overcommunications connection(s) 710.

In some embodiments, computer system 700 also includes input and outputconnections 712, and interfaces and peripheral devices, such as agraphical user interface. Input device(s) are also referred to as userinterface selection devices and include, but are not limited to, akeyboard, a mouse, a pen, a voice input device, a touch input device,etc. Output device(s) are also referred to as displays and include, butare not limited to, cathode ray tube displays, plasma screen displays,liquid crystal screen displays, speakers, printers, etc. These devices,either individually or in combination, connected to input and outputconnections 712 are used to display the information as described herein.All these devices are well known in the art and need not be discussed atlength here.

In some embodiments, the component described herein comprise suchmodules or instructions executable by computer system 700 that may bestored on computer storage medium and other tangible mediums andtransmitted in communication media. Computer storage media includesvolatile and non-volatile, removable and non-removable media implementedin any method or technology for storage of information such as computerreadable instructions, data structures, program modules, or other data.Combinations of any of the above should also be included within thescope of readable media. In some embodiments, computer system 700 ispart of a network that stores data in remote storage media for use bythe computer system 700.

An illustration of an embodiment of the method and system at work willaid in fully understanding the invention. The following description isintended to provide an example of an embodiment of the disclosure andnot to limit the disclosure in any way. An application residing on acomputer system, such as computer system 700 is used to analyzemammograms to determine the likelihood of the presence of cancer. Twoimages, such as a first mammogram image 202 and a second mammogram image208 are inputted into the application. The application proceeds todivide the images into distinct nodes for analysis. In a preferredembodiment, each pixel used to display the picture corresponds to a nodeon the image.

Once the application divides the images into nodes, it begins ananalysis on every node of the first and second image. In an embodiment,the analysis comprises multiple calculations for each node in the image.In an embodiment, the calculations consist of 1.) applying allapplicable classification labels to each node of the images; 2.) mappingnodes of the first image to nodes of the second image; 3.) comparingevery classification label and mapping applied to a node to theclassification labels and mappings applied to surround nodes on the sameimage; 4.) comparing features in the vicinity of a node to typicalcancer features; 5.) comparing features in the vicinity of a node fromthe first image (e.g., the surrounding neighborhood) with features inthe vicinity of the node from the second image which results frommapping of the first node; 6.) calculating a unary potential for everylabel, which consists of classification label and mapping label, foreach node; 7.) calculating a binary potential for every combination oflabels applied to each pair of neighboring nodes. All the calculatedinformation is then input into a CRF model and an algorithm forcalculating Posterior Marginal Probability of labels for each node isapplied. An inference about existence of cancer in one or both images aswell as the location the cancer on these images is made based upon theprobability distribution. In embodiments, the location of cancer isindicated on an image and displayed for a human analysis. In otherembodiments, data produced by the disclosed methods and systems areprovided to another application for further analysis. While aspects ofthe present disclosure have been described as a computer softwareapplication that resides in computer memory or storage, one skilled inthe art will appreciate that aspects of the disclosure can also beperformed by logical hardware components.

This disclosure described some embodiments of the present invention withreference to the accompanying drawings, in which only some of thepossible embodiments were shown. Other aspects may, however, be embodiedin many different forms and should not be construed as limited to theembodiments set forth herein. Rather, these embodiments were provided sothat this disclosure was thorough and complete and fully conveyed thescope of the possible embodiments to those skilled in the art.

Although the embodiments have been described in language specific tostructural features, methodological acts, and computer-readable mediacontaining such acts, it is to be understood that the possibleembodiments, as defined in the appended claims, are not necessarilylimited to the specific structure, acts, or media described. One skilledin the art will recognize other embodiments or improvements that arewithin the scope and spirit of the present invention. Therefore, thespecific structure, acts, or media are disclosed only as illustrativeembodiments. The invention is defined by the appended claims.

1. A computer implemented method for determining a probabilitydistribution of a classification of labels for nodes of a first image bycomparing the first image to a second image, the computer implementedmethod comprising: a. defining a set of labels for a plurality of nodeson the first image including possible classifications for nodes on thefirst and second images and possible mappings for the plurality of nodeson the first image to corresponding nodes on the second image; b.calculating descriptive information for multiple mappings between, andmultiple classification assignments of, the plurality of nodes of thefirst image and corresponding nodes on the second image; c. inputting atleast some of the descriptive information into a probabilistic model; d.determining one or more probabilities of classification for at leastsome of the nodes; and e. providing the determined probabilities ofclassification.
 2. The computer implemented method of claim 1, whereineach label comprises: a mapping part, which indicates a correspondingnode on the second image; and a classification part, which indicates aclassification of the node of the first image and the correspondingsecond node of the second image.
 3. The computer implemented method ofclaim 2, wherein calculating descriptive information further comprisesdetermining a unary potential for at least some of the plurality ofnodes of the first image based upon the labels applied to the pluralityof nodes and determining a binary potential between at least some of theplurality of nodes.
 4. The computer implemented method of claim 3,wherein determining a unary potential for labels applied to at leastsome of the plurality of nodes further comprises: determining the unarypotential of a node on the first image if the node or the correspondingnode on the second image is labeled as cancer; and determining the unarypotential of the node on the first image if the node and thecorresponding node on the second image are labeled as normal.
 5. Thecomputer implemented method of claim 4, wherein determining the unarypotential of the node on the first image if the node or thecorresponding node on the second image is labeled as cancer furthercomprises: comparing values of features associated with the node and thecorresponding node on the second image with typical cancer features; andpenalizing a label assigned to the node if the feature values areatypical.
 6. The computer implemented method of claim 5, wherein thefeatures associated with a node comprise at least one of an intensity, agradient of the intensity, and a contrast.
 7. The computer implementedmethod of claim 4, wherein determining the unary potential of the nodeon the first image if the node and the corresponding node on the secondimage are labeled as normal further comprises: comparing featuresassociated with the node on the first image and the corresponding nodeon the second image; and penalizing the label applied to the first nodefor dissimilarity between the features associated with the first nodeand the second node.
 8. The computer implemented method of claim 7,wherein the features associated with a node comprise at least one of anintensity, a gradient of the intensity, and a contrast.
 9. The computerimplemented method of claim 3, wherein determining the probabilitydistribution further comprises computing the binary potential for a pairof neighboring nodes in the first image and a pair of labels applied tothe pair of neighboring nodes.
 10. The computer implemented method ofclaim 9, wherein computing the binary potential further comprises:penalizing the pair of labels applied to the neighboring nodes fordissimilarity in the mapping parts of the pair labels; and penalizingthe pair of labels applied to the pair of neighboring nodes fordissimilarity in the classification parts of the pair of labels.
 11. Thecomputer implemented method of claim 1, wherein the probabilistic modelis a Conditional Random Field probabilistic model.
 12. The computerimplemented method of claim 1, wherein the step of determining one ormore probabilities comprises estimating Posterior Marginal Probabilityfor each label applied to each node of the first image in the frameworkof a Conditional Random Field model.
 13. The computer implemented methodof claim 1, wherein the step of determining probabilities comprisescalculating a Posterior Marginal Probability of a classification in eachnode by marginalizing over all possible mappings and over all possibleclassifications of corresponding nodes on the other image.
 14. Acomputer implemented method for determining a probability distributionof labels for a first and second mammogram images, the computerimplemented method comprising: determining a probability distribution oflabels for nodes in the first and second mammogram images, whereindetermining the probability distribution comprises: a. defining a set ofpossible mappings of a plurality of nodes on the first image tocorresponding nodes on the second image; b. defining a set of labels ofthe plurality of nodes on the first image; c. calculating descriptiveinformation, for multiple mappings and multiple label assignments of theplurality of nodes of the first image, wherein calculating includes: i.comparing local features of the plurality of nodes of both images to atleast one known value; ii. determining similarity of local features ofat least some of the plurality of nodes on the first image to localfeatures of corresponding nodes on the second image; iii. comparinglabels assigned to at least some of the plurality of nodes in the firstimage to labels assigned to neighboring nodes in the first image; andiv. comparing the mappings of at least some of the plurality of nodes inthe first image to mappings of neighboring nodes in the first image; d.inputting at least some of the descriptive information into aprobabilistic model; and e. determining the probability of a label inany of the plurality of nodes; and f. calculating the probability of aspecific classification in any of the plurality of nodes; and g.providing a best variant of the classification based on the calculatedprobabilities.
 15. The computer implemented method of claim 14, whereinthe labeling module determines a set of possible labels for each node,wherein label comprises: a mapping part, which determines acorresponding node on the second image; and a classification part, whichdetermines whether cancer exists in the node of the first image and thecorresponding second node of the second image.
 16. The computerimplemented method of claim 14, wherein the descriptive informationcomprises a unary potential and a binary potential.
 17. The computerimplemented method of claim 16, further comprising determining a unarypotential for each node of the first image, wherein determining a unarypotential further comprises: determining the unary potential of the nodeon the first image if the node or the corresponding node on the secondimage is labeled as cancer; and determining the unary potential of thenode on the first image if the node and the corresponding node on thesecond image are labeled as normal.
 18. The computer implemented methodof claim 14, wherein determining the probability distribution furthercomprises computing a binary potential for a pair of neighboring nodesin the first image and a pair of combined labels applied to the pair ofneighboring nodes.
 19. A computer storage media encoding a computerprogram of instructions for executing a computer process for a method ofdetermining a probability distribution of labels for first and secondmammogram images, the method comprising: determining a probabilitydistribution of labels for nodes in the first image and the secondimage, wherein the step of determining the probability distributioncomprises: a. defining a set of possible labels and mappings of aplurality of nodes on the first image to corresponding nodes on thesecond image; b. determining a unary potential for each node of thefirst image, wherein determining the unary potential comprises: i.determining the unary potential of a first node on the first image ifthe first node or a corresponding node on the second image is labeledcancer, wherein the unary potential is derived by comparing values offeatures associated with the first node on the first image and thecorresponding node on the second image with at least one known value andpenalizing a label assigned to the first node if feature values areatypical compared to the at least one known value; and ii. determiningthe unary potential of the first node on the first image if the firstnode and the corresponding node on the second image are labeled asnormal, wherein the unary potential is derived by comparing featuresassociated with the first node and the corresponding node on the secondimage and penalizing a label assigned to the first node fordissimilarity between these features; c. determining a binary potentialfor a pair of neighboring nodes in the first image wherein determiningthe binary potential for the pair of neighboring nodes and a pair ofcombined labels applied to the pair of neighboring nodes comprisespenalizing the pair of combined labels applied to the neighboring nodesfor dissimilarity in mapping parts of the pair labels and penalizing thepair of labels applied to the pair of neighboring nodes fordissimilarity in the classification parts of the pair of labels; d.inputting results derived from the calculating the unary and binarypotentials into a probabilistic model, wherein the probabilistic modelis a Conditional Random Field model; and e. computing the probability ofcancer in each node by estimating Posterior Marginal Probability foreach label applied to each node in the framework of the ConditionalRandom Field model and then marginalizing over all possible mappings ofthe node and over labels of its corresponding nodes; and providing theprobable cancer regions.
 20. The method of claim 19, wherein the twomammogram images comprise a first mammogram image of a breast and asecond mammogram image comprising one of an image of the breast taken ata later date or an image of an opposite breast.